# PaperWeeklyAI

**Repository Path**: sureiron/PaperWeeklyAI

## Basic Information

- **Project Name**: PaperWeeklyAI
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-12-09
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

<div id="outputFigDisplay" class="fig">
    <pre id="taag_output_text" style="float:left;" class="flag" contenteditable="true">
    __  ___      _               _ ___    ____                             
   /  |/  /___ _(_)      _____  (_)   |  /  _/    _________  ____ ___      
  / /|_/ / __ `/ / | /| / / _ \/ / /| |  / /_____/ ___/ __ \/ __ `__ \     
 / /  / / /_/ / /| |/ |/ /  __/ / ___ |_/ /_____/ /__/ /_/ / / / / / /     
/_/  /_/\__,_/_/ |__/|__/\___/_/_/  |_/___/     \___/\____/_/ /_/ /_/      
    </pre>
</div>

# PaperWeeklyAI


Studying papers in the fields of computer vision, NLP, and machine learning algorithms every week.


<p align="center">
  <a href="https://charmve.blog.csdn.net/" target="_blank"><img src="https://img.shields.io/badge/blog-Charmve-blue" alt="css"></a>
  <a href="https://charmve.blog.csdn.net/" target="_blank"><img src="https://www.geekxh.com/trending.svg?label=html" alt="html"></a>
  <a href="https://github.com/haizlin/fe-interview/stargazers"><img src="https://img.shields.io/github/stars/geekxh/hello-algorithm.svg" alt="Statr"></a>
</p>
<p align="center">
    紧跟前沿科研动态，每周研读论文！
</p>

<div align=center><img src="https://github.com/Charmve/PaperWeeklyAI/blob/master/MaiweiAI-com.png?raw=true" height="330" width="330"></div>

<div align=center size = 3><b>△微信扫一扫，关注我</b></div>
<br>

从今年三月份开始，我将开源的方向调整为机器学习、计算机视觉、深度学习、NLP、AI前沿技术动态的相关文章发布，从入门学习指导（我自己的机器学习路线总结，入过很多坑）到现在的顶会论文总结、前沿论文研读。公众号主页底部有菜单分类。

<div align=center><img src="https://github.com/ChromeWei/PaperWeeklyAI/blob/master/Menu-MaiweiAI.jpg?raw=true" ></div>
  

<p align="center">
    <b>公众号菜单栏分类</b>
</p>

<br>



### 本项目包括

| 👀 [迈微论文研读](https://github.com/ChromeWei/PaperWeeklyAI)| 🐒 [AI进阶指南](https://github.com/ChromeWei/PaperWeeklyAI/tree/master/00_GuideBooksPDF(English%2BChinese)) | 📚 [超清电子书10000本](https://github.com/ChromeWei/hello-algorithm/tree/master/%E6%B8%85%E6%99%B0%E7%89%88%E7%94%B5%E5%AD%90%E4%B9%A61000%E6%9C%AC) |  🐒 [计算机IT必备](https://github.com/ChromeWei/hello-algorithm/tree/master/%E4%B8%93%E6%A0%8F)  | 🚀 [大厂面经汇总指南](https://github.com/ChromeWei/hello-algorithm/tree/master/%E5%A4%A7%E5%8E%82%E9%9D%A2%E7%BB%8F%E6%B1%87%E6%80%BB100%E7%AF%87) | 
| :--- | :---- | :--- | :--- | :--- |
| 🍄 [NLP论文研读](https://github.com/Charmve/PaperWeeklyAI/tree/master/03_Maiwei%20AI%20PaperWeekly/01_NLP%E8%AE%BA%E6%96%87%E7%A0%94%E8%AF%BB)  | 🌽 [机器学习/深度学习理论篇](https://github.com/Charmve/PaperWeeklyAI/tree/master/00_GuideBooksPDF(English%2BChinese)) | 🐒 [超清思维导图集锦](https://github.com/ChromeWei/hello-algorithm/tree/master/%E8%B6%85%E6%B8%85%E6%80%9D%E7%BB%B4%E5%AF%BC%E5%9B%BE100%E5%BC%A0) | 👺 [专栏：学习os](https://github.com/ChromeWei/hello-algorithm/tree/master/%E4%B8%93%E6%A0%8F/%E6%93%8D%E4%BD%9C%E7%B3%BB%E7%BB%9F)|🍏 [面试：C&C++](https://github.com/ChromeWei/hello-algorithm/tree/master/%E5%A4%A7%E5%8E%82%E9%9D%A2%E7%BB%8F%E6%B1%87%E6%80%BB100%E7%AF%87/C%26C%2B%2B) |
| 🍐 [CV顶会](https://github.com/Charmve/PaperWeeklyAI/tree/master/03_Maiwei%20AI%20PaperWeekly/02_%E8%AE%A1%E7%AE%97%E6%9C%BA%E8%A7%86%E8%A7%89%E8%AE%BA%E6%96%87) | 🍉 [机器学习实战篇](https://github.com/Charmve/PaperWeeklyAI/tree/master/04_AppliedML) | 👺 [电子书：机器学习&深度学习](https://github.com/ChromeWei/PaperWeeklyAI/tree/master/00_GuideBooksPDF(English%2BChinese))|  📡 [专栏：学习网络](https://github.com/ChromeWei/hello-algorithm/tree/master/%E4%B8%93%E6%A0%8F/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%BD%91%E7%BB%9C) | 🍖 [面试：架构师](https://github.com/ChromeWei/hello-algorithm/tree/master/%E5%A4%A7%E5%8E%82%E9%9D%A2%E7%BB%8F%E6%B1%87%E6%80%BB100%E7%AF%87/%E6%9E%B6%E6%9E%84%E5%B8%88)|
| 📡 [AI论文必读篇目10篇](https://github.com/ChromeWei/PaperWeeklyAI/tree/master/00_GuideBooksPDF(English%2BChinese)/05_AI%E8%AE%BA%E6%96%87%E5%BF%85%E8%AF%BB%E7%AF%87%E7%9B%AE10%E7%AF%87) | 📚 [机器学习/Linux电子书](https://github.com/ChromeWei/PaperWeeklyAI/tree/master/00_GuideBooksPDF(English%2BChinese)) | 📝 [电子书：编程与算法](https://github.com/Charmve/PaperWeeklyAI/tree/master/00_GuideBooksPDF(English%2BChinese))  | 📺 [专栏：学习linux](https://github.com/ChromeWei/hello-algorithm/tree/master/%E4%B8%93%E6%A0%8F/Linux) | 🚀 [面试：Python](https://github.com/ChromeWei/hello-algorithm/tree/master/%E5%A4%A7%E5%8E%82%E9%9D%A2%E7%BB%8F%E6%B1%87%E6%80%BB100%E7%AF%87/Python) |🍇 [面试：Java](https://github.com/ChromeWei/hello-algorithm/tree/master/%E5%A4%A7%E5%8E%82%E9%9D%A2%E7%BB%8F%E6%B1%87%E6%80%BB100%E7%AF%87/Java) |
|🎅 [CVPR2020论文30篇](https://github.com/ChromeWei/PaperWeeklyAI/tree/master/00_GuideBooksPDF(English%2BChinese)/06_CVPR2020%E8%AE%BA%E6%96%8730%E7%AF%87)  | 🍏 [ML-Basics](https://github.com/Charmve/ML-Basics) | 📚 [电子书：网络编程](https://github.com/ChromeWei/PaperWeeklyAI/tree/master/00_GuideBooksPDF(English%2BChinese))|🎅 [专栏：学习mysql](https://github.com/ChromeWei/hello-algorithm/tree/master/%E4%B8%93%E6%A0%8F/Mysql)  |🍄 [面试：Mysql](https://github.com/ChromeWei/hello-algorithm/tree/master/%E5%A4%A7%E5%8E%82%E9%9D%A2%E7%BB%8F%E6%B1%87%E6%80%BB100%E7%AF%87/Mysql) |🍅 [面试：前端](https://github.com/ChromeWei/hello-algorithm/tree/master/%E5%A4%A7%E5%8E%82%E9%9D%A2%E7%BB%8F%E6%B1%87%E6%80%BB100%E7%AF%87/%E5%89%8D%E7%AB%AF)  |
| 🌽 [AppliedML](https://github.com/Charmve/PaperWeeklyAI/tree/master/04_AppliedML) | 🍑 [ML-Surveys](https://github.com/Charmve/PaperWeeklyAI/tree/master/06_ML-Surveys)  |  | 🆎 [专栏：学习设计模式](https://github.com/ChromeWei/hello-algorithm/tree/master/%E4%B8%93%E6%A0%8F/%E8%AE%BE%E8%AE%A1%E6%A8%A1%E5%BC%8F) | 📝 [面试：剑指offer](https://github.com/ChromeWei/hello-algorithm/tree/master/%E4%B8%93%E6%A0%8F/%E5%89%91%E6%8C%87offer) |


<br>

<p align="right">
    注：文章链接还没编辑好，现在是测试链接。
</p>



## NLP论文研读
<div class="divcss5" color="lightblue">（点击标题可跳转阅读）</div> 
<br>

| Title | 推荐指数 | 推荐理由 | 时间 |
|   --- |       ---|       ---|  ---|
| [01.李航等提出多粒度AMBERT模型，CLUE、GLUE上优于BERT，中文提升显著](https://github.com/Charmve/PaperWeeklyAI/blob/master/03_Maiwei%20AI%20PaperWeekly/01_NLP%E8%AE%BA%E6%96%87%E7%A0%94%E8%AF%BB/01_%E6%9D%8E%E8%88%AA%E7%AD%89%E6%8F%90%E5%87%BA%E5%A4%9A%E7%B2%92%E5%BA%A6AMBERT%E6%A8%A1%E5%9E%8B%EF%BC%8CCLUE%E3%80%81GLUE%E4%B8%8A%E4%BC%98%E4%BA%8EBERT%EF%BC%8C%E4%B8%AD%E6%96%87%E6%8F%90%E5%8D%87%E6%98%BE%E8%91%97.md) | ⭐⭐⭐⭐⭐|  |  |
| [02.常识知识确能被捕获，西湖大学博士探究BERT如何做常识问答](https://github.com/Charmve/PaperWeeklyAI/tree/master/04_AppliedML)| ⭐⭐⭐⭐ |  |  |     
| [03.图同构下等变、计算高效，韦灵思团队提出「自然图网络」消息传递方法](https://github.com/Charmve/PaperWeeklyAI/tree/master/04_AppliedML) | ⭐⭐⭐ |   |  |
| [04.7 Papers&Radios：SIGIR 2020奖项揭晓;谷歌β-LASSO算法实现最强多层感知机？](https://github.com/Charmve/PaperWeeklyAI/tree/master/04_AppliedML) | ⭐⭐⭐ |   |   |
| [05.文本深度表示模型——word2vec&doc2vec词向量模型](https://github.com/Charmve/PaperWeeklyAI/tree/master/04_AppliedML) | ⭐⭐ |   |   |
| [06.自媒体文章质量如何AI知道，这是微信的自动评估算法](https://github.com/Charmve/PaperWeeklyAI/tree/master/04_AppliedML) | ⭐⭐⭐⭐ |  |  |
| [07.图注意力网络一作：图表征学习在算法推理领域的研究进展](https://github.com/Charmve/PaperWeeklyAI/tree/master/04_AppliedML) | ⭐⭐⭐ |  |  |
| [08.谷歌用算力爆了一篇论文，解答有关无限宽度网络的一切](https://github.com/Charmve/PaperWeeklyAI/tree/master/04_AppliedML) | ⭐⭐⭐ |  |    |

<br>

## 计算机视觉论文

<div class="divcss5" color="lightblue">（点击标题可跳转阅读）</div>
<br>

| Title | 推荐指数 | 推荐理由 | 时间 |
|   --- |       ---|       ---|  ---|
| [01.ImageNet一作、李飞飞高徒邓嘉获最佳论文奖，ECCV 2020奖项全公布](https://github.com/Charmve/PaperWeeklyAI/blob/master/03_Maiwei%20AI%20PaperWeekly/01_NLP%E8%AE%BA%E6%96%87%E7%A0%94%E8%AF%BB/01_%E6%9D%8E%E8%88%AA%E7%AD%89%E6%8F%90%E5%87%BA%E5%A4%9A%E7%B2%92%E5%BA%A6AMBERT%E6%A8%A1%E5%9E%8B%EF%BC%8CCLUE%E3%80%81GLUE%E4%B8%8A%E4%BC%98%E4%BA%8EBERT%EF%BC%8C%E4%B8%AD%E6%96%87%E6%8F%90%E5%8D%87%E6%98%BE%E8%91%97.md) | ⭐⭐⭐⭐⭐|  |  |
| [02.支付宝夺冠CVPR 细粒度视觉分类挑战赛：数据增强+知识蒸馏，效果大幅提升](https://github.com/Charmve/Mirror-Glass-Detection)| ⭐⭐⭐⭐⭐ |  |  |     
| [03.李飞飞团队最新研究，真实场景中识别物体具体属性，连表面纹理都识别出来了](https://github.com/Charmve/Mirror-Glass-Detection) | ⭐⭐⭐⭐ |   |  |
| [04.CVPR2020：真实场景中的玻璃检测，有趣的应用](https://github.com/Charmve/Mirror-Glass-Detection) | ⭐⭐⭐⭐ |   |   |
| [05.表面缺陷检测数据集汇总及其相关项目推荐](https://github.com/Charmve/Mirror-Glass-Detection) | ⭐⭐⭐⭐⭐ |   |   |
| [06.检测、重识别为啥很难一步到位？华中科技大、微软深入挖掘，新方法实现新SOTA](https://github.com/Charmve/Mirror-Glass-Detection) | ⭐⭐⭐⭐ |  |  |
| [07.港中文周博磊团队最新研究：无监督条件下GAN潜在语义识别指南](https://github.com/Charmve/Mirror-Glass-Detection) | ⭐⭐⭐ |  |  |
| [08.一张“静态”图实现3D人脸建模！这是中科院博士生入选ECCV的新研究(开源)](https://github.com/Charmve/Mirror-Glass-Detection) | ⭐⭐⭐ |  |    |
| [09.ECCV 2020:再见，迁移学习？可解释和泛化的行人再辨识](https://github.com/Charmve/PaperWeeklyAI) | ⭐⭐⭐ |  |    |
| [10.检测、重识别为啥很难一步到位？华中科技大、微软深入挖掘，新方法实现新SOTA](https://github.com/Charmve/Mirror-Glass-Detection) | ⭐⭐⭐ |  |    ||    |
| [11.无需标注数据集，自监督注意力机制就能搞定目标跟踪](https://github.com/Charmve/Mirror-Glass-Detection) | ⭐⭐⭐⭐ |  |    |
| [12.准确检测DeepFake视频，阿里新算法从多个人物中识别被篡改的人脸](https://github.com/Charmve/Mirror-Glass-Detection) | ⭐⭐⭐ |  |    |
| [13.CVPR 2020最佳学生论文分享回顾：通过二叉空间分割（BSP）生成紧凑3D网格](https://github.com/Charmve/Mirror-Glass-Detection) | ⭐⭐⭐ |  |    |
| [14.卷积神经网络必读的40篇经典论文，包含检测/识别/分类/分割多个领域](https://github.com/Charmve/Mirror-Glass-Detection) | ⭐⭐⭐⭐ |  |    |
| [15.Kaggle X光肺炎检测比赛第二名方案解析 CVPR 2020 Workshop](https://github.com/Charmve/PaperWeeklyAI/blob/master/03_Maiwei%20AI%20PaperWeekly/02_%E8%AE%A1%E7%AE%97%E6%9C%BA%E8%A7%86%E8%A7%89%E8%AE%BA%E6%96%87/%E2%80%8BKaggle%20X%E5%85%89%E8%82%BA%E7%82%8E%E6%A3%80%E6%B5%8B%E6%AF%94%E8%B5%9B%E7%AC%AC%E4%BA%8C%E5%90%8D%E6%96%B9%E6%A1%88%E8%A7%A3%E6%9E%90%20%7C%20CVPR%202020%20Workshop.md) | ⭐⭐⭐⭐ |  |    |

<br>

## 机器学习/深度学习理论

<div class="divcss5" color="lightblue">（点击标题可跳转阅读）</div>
<br>

| Title | 推荐指数 | 推荐理由 | 时间 |
|   --- |       ---|       ---|  ---|
| [01.机器学习算法之——K最近邻(k-Nearest Neighbor，KNN)分类算法原理讲解](https://github.com/Charmve/PaperWeeklyAI/blob/master/03_Maiwei%20AI%20PaperWeekly/03_%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0&%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E7%90%86%E8%AE%BA/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E7%AE%97%E6%B3%95%E4%B9%8B%E2%80%94%E2%80%94K%E6%9C%80%E8%BF%91%E9%82%BB(k-Nearest%20Neighbor%EF%BC%8CKNN)%E5%88%86%E7%B1%BB%E7%AE%97%E6%B3%95%E5%8E%9F%E7%90%86%E8%AE%B2%E8%A7%A3.md) | ⭐⭐⭐⭐⭐|  |  |
| [02.机器学习算法之——隐马尔可夫模型(Hidden Markov Models,HMM)](https://github.com/Charmve/PaperWeeklyAI)| ⭐⭐⭐⭐ |  |  |     
| [03.机器学习算法之——K最近邻(k-Nearest Neighbor，KNN)分类算法Python实现](https://github.com/Charmve/PaperWeeklyAI/blob/master/03_Maiwei%20AI%20PaperWeekly/03_%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%26%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E7%90%86%E8%AE%BA/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E7%AE%97%E6%B3%95%E4%B9%8B%E2%80%94%E2%80%94K%E6%9C%80%E8%BF%91%E9%82%BB(k-Nearest%20Neighbor%EF%BC%8CKNN)%E5%88%86%E7%B1%BB%E7%AE%97%E6%B3%95Python%E5%AE%9E%E7%8E%B0.md) | ⭐⭐⭐ |   |  |
| [04.机器学习算法之——走进卷积神经网络(CNN)](https://github.com/Charmve/PaperWeeklyAI) | ⭐⭐⭐ |   |   |
| [05.机器学习算法之——逻辑回归（Logistic Regression）](https://github.com/Charmve/PaperWeeklyAI) | ⭐⭐⭐ |   |   |
| [06.暴力方法将成过去？UC伯克利等新研究返璞归真，探索网络的本质](https://github.com/Charmve/PaperWeeklyAI) | ⭐⭐⭐⭐ |  |  |
| [07.机器学习中的最优化算法总结](https://github.com/Charmve/PaperWeeklyAI) | ⭐⭐⭐ |  |  |
| [08.超越标准 GNN ！DeepMind、谷歌提出图匹配网络(ICML最新论文)](https://github.com/Charmve/PaperWeeklyAI) | ⭐⭐⭐ |  |    |
| [09.机器学习算法常用评价指标总结](https://github.com/Charmve/PaperWeeklyAI/tree/master/03_Maiwei%20AI%20PaperWeekly) | ⭐⭐⭐⭐ |  |    |
| [10.损失函数的可视化：浅论模型的参数空间与正则](https://github.com/Charmve/PaperWeeklyAI/tree/master/03_Maiwei%20AI%20PaperWeekly) | ⭐⭐⭐ |  |    |
| [11.深度学习中“消失的梯度”](https://github.com/Charmve/PaperWeeklyAI/tree/master/03_Maiwei%20AI%20PaperWeekly) | ⭐⭐⭐ |  |    |
| [12.预、自训练之争：谷歌说预训练虽火，但在标注数据上自训练更有效](https://github.com/Charmve/PaperWeeklyAI/tree/master/03_Maiwei%20AI%20PaperWeekly) | ⭐⭐⭐ |  |    |

<br>

## 机器学习实战篇
<div class="divcss5" color="lightblue">（点击标题可跳转阅读）</div>
<br>

| Title | 推荐指数 | 推荐理由 | 时间 |
|   --- |       ---|       ---|  ---|
| [01.机器学习实战：逻辑回归应用之“Kaggle房价预测”](https://github.com/Charmve/PaperWeeklyAI) | ⭐⭐⭐⭐|  |  |
| [02.机器学习实战：逻辑回归应用之“Kaggle泰坦尼克之灾”](https://github.com/Charmve/PaperWeeklyAI/blob/master/03_Maiwei%20AI%20PaperWeekly/04_%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E5%AE%9E%E6%88%98%E7%AF%87/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E5%AE%9E%E6%88%98%20%7C%20%E9%80%BB%E8%BE%91%E5%9B%9E%E5%BD%92%E5%BA%94%E7%94%A8%E4%B9%8B%E2%80%9CKaggle%E6%B3%B0%E5%9D%A6%E5%B0%BC%E5%85%8B%E4%B9%8B%E7%81%BE%E2%80%9D.md)| ⭐⭐⭐⭐⭐ |  |  |     
| [03.PyTorch实战：使用卷积神经网络对CIFAR10图片进行分类（附源码）](https://github.com/Charmve/PaperWeeklyAI) | ⭐⭐⭐⭐ |   |  |
| [04.表情识别FER：基于深度学习的人脸表情识别系统（Keras）](https://github.com/Charmve/PaperWeeklyAI) | ⭐⭐⭐ |   |   |
| [05.CVPR 2020夜间行人检测挑战赛两冠一亚：DeepBlueAI团队获胜方案解读](https://github.com/Charmve/PaperWeeklyAI) | ⭐⭐⭐ |   |   |
| [06.本科生晋升GM记录：Kaggle比赛进阶技巧分享](https://github.com/Charmve/PaperWeeklyAI) | ⭐⭐⭐⭐ |  |  |
| [07.机器学习第一步，这是一篇手把手的随机森林入门实战](https://github.com/Charmve/PaperWeeklyAI) | ⭐⭐⭐ |  |  |
| [08.为什么你的模型效果这么差，深度学习调参有哪些技巧？识](https://github.com/Charmve) | ⭐⭐⭐⭐ |  |    |
| [09.你还在手动调参？自动化深度学习了解一下！(CVPR2020 Tutorial)](https://github.com/Charmve) | ⭐⭐⭐⭐ |  |    |

<br>

## AI进阶指南
<div class="divcss5" color="lightblue">（点击标题可跳转阅读）</div>
<br>

| Title | 推荐指数 | 推荐理由 | 时间 |
|   --- |       ---|       ---|  ---|
| [01.超赞的PyTorch资源大列表，GitHub标星9.4k+，中文版也上线了](https://github.com/Charmve) | ⭐⭐⭐⭐⭐|  |  |
| [02.GitHub 上有哪些适合新手跟进的优质项目？](https://github.com/Charmve)| ⭐⭐⭐⭐⭐ |  |  |     
| [03.实至名归！NumPy 官方早有中文教程，结合深度学习，还有防脱发指南](https://github.com/Charmve) | ⭐⭐⭐⭐ |   |  |
| [04.GitHub标星23k+，从零开始的深度学习实用教程「PyTorch官方推荐」](https://github.com/Charmve) | ⭐⭐⭐ |   |   |
| [05.吴恩达关于机器学习职业生涯以及阅读论文的一些建议（附AI领域必读的10篇论文PDF）](https://github.com/Charmve) | ⭐⭐⭐⭐ |   |   |
| [06.有了这些珍藏的实用工具/学习网站，自学更快乐！](https://github.com/Charmve) | ⭐⭐⭐⭐⭐ |  |  |
| [07.你想要的高清电子书都在这里！免费下载！](https://github.com/Charmve) | ⭐⭐⭐ |  |  |
| [08.机器学习进阶经典著作推荐(提供免费下载)](https://github.com/Charmve) | ⭐⭐⭐⭐ |  |    |
| [09.机器学习/数据科学从入门到精通：必经的5个阶段，你处于那个阶段？](https://github.com/Charmve) | ⭐⭐⭐ |  |    |

<br>

## 迈微AI前沿(精选)

<div class="divcss5" color="lightblue">（点击标题可跳转阅读）</div>
<br>

| Title | 推荐指数 | 推荐理由 | 时间 |
|   --- |       ---|       ---|  ---|
| [01.用反向传播算法解释大脑学习过程？Hinton等人新研究登上Nature子刊](https://github.com/Charmve/Design-of-a-3D-Dynamic-Display-System-Based-on-Voice-Control) | ⭐⭐⭐⭐|  |  |
| [02.工作007，8天完成688次实验，独立发现催化剂：机器人研究员登上Nature封面](https://github.com/Charmve/Design-of-a-3D-Dynamic-Display-System-Based-on-Voice-Control)| ⭐⭐⭐⭐ |  |  |     
| [03.《Nature》子刊：不仅是语言，机器翻译还能把脑波「翻译」成文字](https://github.com/Charmve/Design-of-a-3D-Dynamic-Display-System-Based-on-Voice-Control) | ⭐⭐⭐ |   |  |
| [04.普林、DeepMind新研究：结合深度学习和符号回归，从深度模型中看见宇宙](https://github.com/Charmve/Design-of-a-3D-Dynamic-Display-System-Based-on-Voice-Control) | ⭐⭐⭐ |   |   |
| [05.DeepMind、哈佛造出了 AI「小白鼠」，从跑、跳、觅食、击球窥探神经网络的奥秘](https://github.com/Charmve/Design-of-a-3D-Dynamic-Display-System-Based-on-Voice-Control) | ⭐⭐⭐ |   |   |
| [06.马斯克放话：6个月内公测“卫星互联网”！颠覆5G的将不是6G](https://github.com/Charmve/Design-of-a-3D-Dynamic-Display-System-Based-on-Voice-Control) | ⭐⭐ |  |  |
| [07.谷歌量子计算突破登Science封面！首次对化学反应进行量子模拟](https://github.com/Charmve/Design-of-a-3D-Dynamic-Display-System-Based-on-Voice-Control) | ⭐⭐⭐ |  |  |
| [08.马斯克活猪脑机接口试验成功！多芯片植入，硬币大小，实时读取脑电波](https://github.com/Charmve/Design-of-a-3D-Dynamic-Display-System-Based-on-Voice-Control) | ⭐⭐⭐ |  |    |
| [10.Science：AI领域那么多引人注目的「进展」，竟是无用功](https://github.com/Charmve/Design-of-a-3D-Dynamic-Display-System-Based-on-Voice-Control) | ⭐⭐⭐⭐ |  |    |
| [11.改改Python代码，运行速度还能提升6万倍，Science：先别想摩尔定律了](https://github.com/Charmve/Design-of-a-3D-Dynamic-Display-System-Based-on-Voice-Control) | ⭐⭐⭐ |  |    |
| [12.量子计算机遇到新对手？随机磁电路，因数分解更厉害](https://github.com/Charmve/Design-of-a-3D-Dynamic-Display-System-Based-on-Voice-Control) | ⭐⭐⭐⭐ |  |    |


<br>

想要获得更多精彩内容，请关注微信公众号：迈微AI研习社

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